Finally, given a real network, coordinates of the nodes can be inferred using a combination of machine-learning and maximum likelihood estimation techniques. In particular here we rely in the embedding method Mercator [1]}, the most solid algorithm currently available in the literature. It enables us to identify the set of coordinates that maximize the probability that the observed real-world network was generated by the described geometric models. The inferred geometric maps have proven to be very accurate in the case of scale-free highly clustered networks [2]}, [3]}
